Fix! Blackified

src/openeo_gfmap/fetching/commons.py

@@ -128,8 +128,7 @@ def load_collection(
     pre_mask = params.get("pre_mask", None)
     if pre_mask is not None:
         assert isinstance(pre_mask, openeo.DataCube), (
-            f"The 'pre_mask' parameter must be an openeo datacube, "
-            f"got {pre_mask}."
+            f"The 'pre_mask' parameter must be an openeo datacube, " f"got {pre_mask}."
         )
         cube = cube.mask(pre_mask.resample_cube_spatial(cube))
 

src/openeo_gfmap/fetching/s1.py

@@ -113,9 +113,7 @@ def s1_grd_default_processor(cube: openeo.DataCube, **params):
         )
 
         cube = resample_reproject(
-            cube,
-            params.get("target_resolution", 10.0),
-            params.get("target_crs", None)
+            cube, params.get("target_resolution", 10.0), params.get("target_crs", None)
         )
 
         cube = rename_bands(cube, BASE_SENTINEL1_GRD_MAPPING)

src/openeo_gfmap/preprocessing/compositing.py

@@ -7,16 +7,25 @@
 import openeo
 
 
-def median_compositing(cube: openeo.DataCube, period: Union[str, list]) -> openeo.DataCube:
+def median_compositing(
+    cube: openeo.DataCube, period: Union[str, list]
+) -> openeo.DataCube:
     """Perfrom median compositing on the given datacube."""
     if isinstance(period, str):
-        return cube.aggregate_temporal_period(period=period, reducer="median", dimension="t")
+        return cube.aggregate_temporal_period(
+            period=period, reducer="median", dimension="t"
+        )
     elif isinstance(period, list):
-        return cube.aggregate_temporal(intervals=period, reducer="median", dimension="t")
+        return cube.aggregate_temporal(
+            intervals=period, reducer="median", dimension="t"
+        )
+
 
 def mean_compositing(cube: openeo.DataCube, period: str) -> openeo.DataCube:
     """Perfrom mean compositing on the given datacube."""
     if isinstance(period, str):
-        return cube.aggregate_temporal_period(period=period, reducer="mean", dimension="t")
+        return cube.aggregate_temporal_period(
+            period=period, reducer="mean", dimension="t"
+        )
     elif isinstance(period, list):
         return cube.aggregate_temporal(intervals=period, reducer="mean", dimension="t")

src/openeo_gfmap/preprocessing/interpolation.py

@@ -4,6 +4,8 @@
 import openeo
 
 
-def linear_interpolation(cube: openeo.DataCube,) -> openeo.DataCube:
+def linear_interpolation(
+    cube: openeo.DataCube,
+) -> openeo.DataCube:
     """Perform linear interpolation on the given datacube."""
     return cube.apply_dimension(dimension="t", process="array_interpolate_linear")

src/openeo_gfmap/preprocessing/udf_rank.py

@@ -1,4 +1,3 @@
-
 import numpy as np
 import xarray as xr
 from openeo.udf import XarrayDataCube
@@ -31,9 +30,7 @@ def select_maximum(score: xr.DataArray):
         # Convert YYYY-mm-dd to datetime64 objects
         time_bins = [np.datetime64(interval[0]) for interval in intervals]
 
-        rank_mask = bap_score.groupby_bins('t', bins=time_bins).map(
-            select_maximum
-        )
+        rank_mask = bap_score.groupby_bins("t", bins=time_bins).map(select_maximum)
     else:
         raise ValueError("Period is not defined in the UDF. Cannot run it.")
 

tests/test_openeo_gfmap/test_cloud_masking.py

@@ -124,12 +124,8 @@ def test_bap_quintad(backend: Backend):
     backend_context = BackendContext(backend=backend)
 
     # Additional parameters
-    fetching_parameters = {
-        "fetching_resolution": 10.0
-    }
-    preprocessing_parameters = {
-        "apply_scl_dilation": True
-    }
+    fetching_parameters = {"fetching_resolution": 10.0}
+    preprocessing_parameters = {"apply_scl_dilation": True}
 
     # Fetch the datacube
     s2_extractor = build_sentinel2_l2a_extractor(